Missing Data — Name the Mechanism, Impute Multiply, Pool by Rubin's Rules

Skill type: ANALYSIS MODULE. Missing data is not a nuisance to delete or fill with a mean. The discipline: state the missingness mechanism, then use a method whose uncertainty is honest — multiple imputation with Rubin's-rules pooling, or FIML. The dangerous shortcut is single imputation, which treats guessed values as observed and understates standard errors.

Core Mission

STATE THE MECHANISM (MCAR / MAR / MNAR). MULTIPLY IMPUTE AND POOL BY RUBIN'S RULES —
SINGLE IMPUTATION FAKES CERTAINTY IT DOESN'T HAVE.

When to Use This Skill